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测试一种创新的无标记技术在定量和定性步态分析中的性能。

Testing the Performance of an Innovative Markerless Technique for Quantitative and Qualitative Gait Analysis.

机构信息

Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37129 Verona, Italy.

IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143 Florence, Italy.

出版信息

Sensors (Basel). 2020 Nov 20;20(22):6654. doi: 10.3390/s20226654.

Abstract

Gait abnormalities such as high stride and step frequency/cadence (SF-stride/second, CAD-step/second), stride variability (SV) and low harmony may increase the risk of injuries and be a sentinel of medical conditions. This research aims to present a new markerless video-based technology for quantitative and qualitative gait analysis. 86 healthy individuals (mead age 32 years) performed a 90 s test on treadmill at self-selected walking speed. We measured SF and CAD by a photoelectric sensors system; then, we calculated average ± standard deviation (SD) and within-subject coefficient of variation (CV) of SF as an index of SV. We also recorded a 60 fps video of the patient. With a custom-designed web-based video analysis software, we performed a spectral analysis of the brightness over time for each pixel of the image, that reinstituted the frequency contents of the videos. The two main frequency contents (F1 and F2) from this analysis should reflect the forcing/dominant variables, i.e., SF and CAD. Then, a harmony index (HI) was calculated, that should reflect the proportion of the pixels of the image that move consistently with F1 or its supraharmonics. The higher the HI value, the less variable the gait. The correspondence SF-F1 and CAD-F2 was evaluated with both paired t-Test and correlation and the relationship between SV and HI with correlation. SF and CAD were not significantly different from and highly correlated with F1 (0.893 ± 0.080 Hz vs. 0.895 ± 0.084 Hz, < 0.001, r = 0.99) and F2 (1.787 ± 0.163 Hz vs. 1.791 ± 0.165 Hz, < 0.001, r = 0.97). The SV was 1.84% ± 0.66% and it was significantly and moderately correlated with HI (0.082 ± 0.028, < 0.001, r = 0.13). The innovative video-based technique of global, markerless gait analysis proposed in our study accurately identifies the main frequency contents and the variability of gait in healthy individuals, thus providing a time-efficient, low-cost means to quantitatively and qualitatively study human locomotion.

摘要

步态异常,如高步幅和步频/步速(SF-步/秒,CAD-步/秒)、步幅变异性(SV)和低协调性,可能会增加受伤的风险,并成为身体状况的一个信号。本研究旨在介绍一种新的无标记视频步态分析技术。86 名健康个体(平均年龄 32 岁)在跑步机上以自我选择的步行速度进行 90 秒测试。我们使用光电传感器系统测量 SF 和 CAD,然后计算 SF 的平均值±标准差(SD)和个体内变异系数(CV),作为 SV 的指标。我们还记录了患者的 60 fps 视频。使用定制的基于网络的视频分析软件,我们对图像中每个像素的亮度随时间进行了频谱分析,从而重建了视频的频率内容。从该分析得到的两个主要频率内容(F1 和 F2)应反映强制/主导变量,即 SF 和 CAD。然后,计算了一个协调指数(HI),该指数应反映图像中与 F1 或其超谐波一致移动的像素比例。HI 值越高,步态的变化越小。使用配对 t 检验和相关性评估了 SF-F1 和 CAD-F2 的对应关系,并用相关性评估了 SV 和 HI 之间的关系。SF 和 CAD 与 F1(0.893±0.080 Hz 对 0.895±0.084 Hz,<0.001,r=0.99)和 F2(1.787±0.163 Hz 对 1.791±0.165 Hz,<0.001,r=0.97)差异无统计学意义且高度相关。SV 为 1.84%±0.66%,与 HI 显著中度相关(0.082±0.028,<0.001,r=0.13)。本研究中提出的基于视频的全局、无标记步态分析新技术准确地识别了健康个体的主要步态频率内容和变异性,从而提供了一种高效、低成本的方法来定量和定性地研究人类运动。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8250/7699971/06772600c7b2/sensors-20-06654-g001.jpg

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